Maggie Mi


2024

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ShefCDTeam at SemEval-2024 Task 4: A Text-to-Text Model for Multi-Label Classification
Meredith Gibbons | Maggie Mi | Xingyi Song | Aline Villavicencio
Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)

This paper presents our findings for SemEval2024 Task 4. We submit only to subtask 1, applying the text-to-text framework using a FLAN-T5 model with a combination of parameter efficient fine-tuning methods - low-rankadaptation and prompt tuning. Overall, we find that the system performs well in English, but performance is limited in Bulgarian, North Macedonian and Arabic. Our analysis raises interesting questions about the effects of labelorder and label names when applying the text-to-text framework.

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Sign of the Times: Evaluating the use of Large Language Models for Idiomaticity Detection
Dylan Phelps | Thomas M. R. Pickard | Maggie Mi | Edward Gow-Smith | Aline Villavicencio
Proceedings of the Joint Workshop on Multiword Expressions and Universal Dependencies (MWE-UD) @ LREC-COLING 2024

Despite the recent ubiquity of large language models and their high zero-shot prompted performance across a wide range of tasks, it is still not known how well they perform on tasks which require processing of potentially idiomatic language. In particular, how well do such models perform in comparison to encoder-only models fine-tuned specifically for idiomaticity tasks? In this work, we attempt to answer this question by looking at the performance of a range of LLMs (both local and software-as-a-service models) on three idiomaticity datasets: SemEval 2022 Task 2a, FLUTE, and MAGPIE. Overall, we find that whilst these models do give competitive performance, they do not match the results of fine-tuned task-specific models, even at the largest scales (e.g. for GPT-4). Nevertheless, we do see consistent performance improvements across model scale. Additionally, we investigate prompting approaches to improve performance, and discuss the practicalities of using LLMs for these tasks.

2023

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Mmi01 at The BabyLM Challenge: Linguistically Motivated Curriculum Learning for Pretraining in Low-Resource Settings
Maggie Mi
Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning